New IPPR analysis shows that £290billion wages associated with jobs that could be automated

The total level of wages associated with jobs that have the technical potential to be automated in the UK is £290 billion per year, which represents 33% of all wages and earnings from labour in the economy, according to a new report published today by IPPR, the progressive policy think tank, for the IPPR Commission on Economic Justice. The report further shows that low-wage jobs have more potential to be automated than high-wage jobs.

The new report from IPPR shows that work will be transformed, not eliminated, by automation. The new analysis shows that it’s not just automation’s impact on the number of jobs that need to be considered but the impact on inequality. If automation leads to lower average wages or working hours, or loss of jobs in aggregate, a significant amount of national income could be transferred from wages to profits. Andwhile increased automation of activities will replace some workers and labour earnings, employment and wages will rise in other areas of the labour market due to higher output and productivity, offsetting some of the original £290 billion lost but increasing pay inequality.

The report calls for government to manage a fair acceleration of automation, so that the benefits and threats to future labour market are shared. Without effective management from the government inequality is likely to increase because of unequal ownership of capital and highly-skilled workers being able to command higher wages and better jobs

The report highlights that, despite fears, we are not on the cusp of a ‘post-human’ economy and that automation could produce significant productivity gains that will reshape specific sectors and occupations. Most jobs will likely be reallocated rather than eliminated, economic output will increase, and new sources of wealth will be created. And so the biggest challenge that automation brings will be in the fair distribution of its rewards.

The analysis shows that if the benefits of automation are fairly shared the automation can be a key part of building an economy where prosperity is underpinned by justice. But if automation is managed poorly, automation could create a ‘paradox of plenty’: society would be far richer in aggregate, but, for many individuals and communities, technological change could reinforce inequalities of power and reward.

There will also be winners and losers, depending on where people live, their gender and ethnicity. While 39% of jobs in London have high technical potential for automation, 48% of jobs in the North East and Northern Ireland do.

Not only is managing automation important for the way in which the benefits and impacts are distribution but it is needed to reap the full productivity benefits, which in turn will enable higher wages. Due to the UK’s low investment rates, poor management practices, and long tail of low-wage, low-productivity firms, it is the relative absence of robots in the UK economy, not their imminent rise, which is the biggest challenge. But if managed effectively automation can deliver a powerful boost to UK productivity. An accelerated trajectory of automation could raise productivity growth by between 0.8 to 1.4% annually, boosting GDP by 10% by 2030.

IPPR therefore recommends:

Managed acceleration of automation: To help realise the benefits of technological advances as well as the adoption of digital technologies throughout the economy:

- The more rapid adoption of digital technologies, including automation, should become one of the national ‘missions’ of the Government’s industrial strategy. The goal should be to make the UK the most digitally advanced economy in the world by 2040.

- A new partnership body, Productivity UK, should be established with the goal of raising firm-level productivity, including the acceleration of investment in automation technologies. It should focus on the wider adoption of digital and other technologies throughout the economy.

- The UK skills system needs to better equip people with skills to complement automating technologies and retrain where jobs are lost. The apprenticeships levy should be turned into a ‘productivity and skills levy’ that firms can use for wider skills training and utilisation. A personal retraining allowance should be introduced for workers made redundant.

New public institutions: to inform and regulate how automating technologies are used and to ensure that society responds proactively to the profound ethical issues raised by robotics and artificial intelligence.

An Authority for the Ethical Use of Robotics and Artificial Intelligence should be established to regulate the use of automating technologies. The proposed authority should make recommendations to Government and business on the governance and use of robots and AI. It should be modelled on the Human Fertilisation and Embryology Authority (HFEA) that regulates embryonic technologies, ensuring that society determine the rules and ethical frameworks governing autonomous technologies.

New models of collective ownership: to ensure that everyone has a claim on the dividends of technological change, to enable automation to work for the common good.

New models of capital ownership are needed to ensure automation broadens prosperity rather than concentrates wealth. These could include a Citizens’ Wealth Fund that owns a broad portfolio of assets on behalf of the public and pays out a universal capital dividend. It could also include the creation of employee ownership trusts to give workers a stronger stake in the firms for which they work.

Mathew Lawrence, IPPR Senior Research Fellow said:

“Despite the rhetoric of the rise of the robots, machines aren’t about to take all our jobs. While technological change will reshape how we work and what we do, it won’t eliminate employment. A bigger challenge is arguably the effect of automation on inequality in the UK. Managed badly, the benefits of automation could be narrowly concentrated, benefitting those who own capital and highly skilled workers. Inequality would spiral. Managed well though, with a strategy to increase adoption of technologies in the everyday economy and new models of ownership to spread the benefits, automation could help create a future of shared economic plenty.”

Carys Roberts, IPPR Research Fellow said:“Our analysis shows that jobs with the potential to be automated are associated with £290 billion of wages each year. Much of this will be replaced through increased wages due to higher productivity and new jobs created, but a substantial portion could also be transferred from wages to profits. Within that, some people will get a payrise while others are trapped in low pay, low productivity sectors. To avoid inequality rising, the government should look at ways to spread capital ownership, and make sure everyone benefits from increased automation.”

While increased automation of activities will replace some workers and labour earnings, employment and wages will rise in other areas of the labour market due to higher output and productivity. The final value of the transfer from wages to capital is hard to predict and in large part it will depend on the extent of friction in the labour market: whereby displaced workers are unable to move or retrain to find work, causing an oversupply of labour in certain geographies and sectors which results in high unemployment and suppressed pay. We would expect the final figure to be some part of, but lower than, the full £290 billion.

To estimate the wages associated with jobs that could be automated, we assign UK minor occupation codes probabilities of computerisation using Frey and Osborne’s US paper (2013). For the small proportion of jobs for which we do not have probabilities of computerisation, we assume zero probability.

Our total for wages is the sum of the number of jobs in each occupation multiplied by probability of computerisation and the mean gross annual income for that occupation, using the Labour Force Survey (2016-2017) and ASHE (2017). This approach follows that taken by the Bank of England (2015). An alternative approach that other researchers including Frey and Osborne have taken is to identify a cut-off for ‘high-potential’ jobs, such as probability higher than or equal to 0.7, and to estimate the number of jobs and wages associated with these occupations, however, selecting a cut-off could be seen as arbitrary. Some results may differ from other researchers’ due to this methodological choice.

Wage totals are grossed using factors based on the difference between our estimates for total earnings in the economy and ONS estimates (OBR 2017).

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